融合类别和结构信息的多尺度协同耦合度量学习方法

Multi-scale Collaborative Coupled Metric Learning Method Based on the Fusion of Class and Structure Information

针对不同尺度空间集合中数据样本无法直接匹配的问题,提出融合类别和结构信息的多尺度协同耦合度量学习方法.首先将类别信息作为主要监督信息,样本分布结构信息作为辅助监督信息,构建相关关系矩阵.然后基于该相关关系矩阵构建线性和非线性最优化目标方程,通过最优化目标方程求解将不同尺度数据集合中的数据样本变换至尺度统一的公共空间,最终实现不同尺度空间中数据样本的度量.人脸识别的实验表明,多尺度空间的非线性协同耦合度量是一种有效的度量方法,运算简单方便,能够获得较高的识别率.

Aiming at the elements matching problem in different scale space sets, multi-scale collaborative coupled metric learning method based on the fusion of class and structure information is proposed. Firstly, the correlation matrix is constructed under the guidance of class information and structure information of sample distribution. The class information is significant for supervision and the structure information is the auxiliary supervision information. The linear and nonlinear optimization objective equations are constructed based on the correlation matrix. By solving the optimization objective equation, the samples are transformed from different scale space datasets into a unified public space for distance measurement. The experimental results of face recognition show that the nonlinear collaborative coupled metric is an effective measurement method and it is simple and convenient with a higher recognition rate.